Zhe Li

Learning From Brains How to Regularize Machines

Despite impressive performance on numerous visual tasks, Convolutional N...
11/11/2019 ∙ by Zhe Li, et al. ∙ 36 ∙ share

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Generative Adversarial Active Learning for Unsupervised Outlier Detection

Outlier detection is an important topic in machine learning and has been...
09/28/2018 ∙ by Yezheng Liu, et al. ∙ 18 ∙ share

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Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network

Automatic diagnosing lung cancer from Computed Tomography (CT) scans inv...
11/22/2017 ∙ by Fangzhou Liao, et al. ∙ 0 ∙ share

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Thoracic Disease Identification and Localization with Limited Supervision

Accurate identification and localization of abnormalities from radiology...
11/17/2017 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer

In this paper, we present a simple analysis of fast rates with high pr...
09/09/2017 ∙ by Tianbao Yang, et al. ∙ 0 ∙ share

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CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices

Large-scale deep neural networks (DNNs) are both compute and memory inte...
08/29/2017 ∙ by Caiwen Ding, et al. ∙ 0 ∙ share

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SEP-Nets: Small and Effective Pattern Networks

While going deeper has been witnessed to improve the performance of conv...
06/13/2017 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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Hardware-Driven Nonlinear Activation for Stochastic Computing Based Deep Convolutional Neural Networks

Recently, Deep Convolutional Neural Networks (DCNNs) have made unprecede...
03/12/2017 ∙ by Ji Li, et al. ∙ 0 ∙ share

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Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank

Recently low displacement rank (LDR) matrices, or so-called structured m...
03/01/2017 ∙ by Liang Zhao, et al. ∙ 0 ∙ share

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Unified Convergence Analysis of Stochastic Momentum Methods for Convex and Non-convex Optimization

Recently, stochastic momentum methods have been widely adopted in train...
04/12/2016 ∙ by Tianbao Yang, et al. ∙ 0 ∙ share

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SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

With recent advancing of Internet of Things (IoTs), it becomes very attr...
11/18/2016 ∙ by Ao Ren, et al. ∙ 0 ∙ share

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Improved Dropout for Shallow and Deep Learning

Dropout has been witnessed with great success in training deep neural ne...
02/06/2016 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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Image Dataset for Visual Objects Classification in 3D Printing

The rapid development in additive manufacturing (AM), also known as 3D p...
02/15/2018 ∙ by Hongjia Li, et al. ∙ 0 ∙ share

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Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework

Hardware accelerations of deep learning systems have been extensively in...
02/18/2018 ∙ by Yanzhi Wang, et al. ∙ 0 ∙ share

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Efficient Recurrent Neural Networks using Structured Matrices in FPGAs

Recurrent Neural Networks (RNNs) are becoming increasingly important for...
03/20/2018 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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C3PO: Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing

Increasing malicious users have sought practices to leverage 3D printing...
03/20/2018 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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An Area and Energy Efficient Design of Domain-Wall Memory-Based Deep Convolutional Neural Networks using Stochastic Computing

With recent trend of wearable devices and Internet of Things (IoTs), it ...
02/03/2018 ∙ by Xiaolong Ma, et al. ∙ 0 ∙ share

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A Framework in CRM Customer Lifecycle: Identify Downward Trend and Potential Issues Detection

Customer retention is one of the primary goals in the area of customer r...
02/25/2018 ∙ by Kun Hu, et al. ∙ 0 ∙ share

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Learning Topics using Semantic Locality

The topic modeling discovers the latent topic probability of the given t...
04/11/2018 ∙ by Ziyi Zhao, et al. ∙ 0 ∙ share

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Towards Budget-Driven Hardware Optimization for Deep Convolutional Neural Networks using Stochastic Computing

Recently, Deep Convolutional Neural Network (DCNN) has achieved tremendo...
05/10/2018 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints

Recently, there emerged revived interests of designing automatic program...
06/03/2018 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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A Unified Analysis of Stochastic Momentum Methods for Deep Learning

Stochastic momentum methods have been widely adopted in training deep ne...
08/30/2018 ∙ by Yan Yan, et al. ∙ 0 ∙ share

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EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching

Designing the structure of neural networks is considered one of the most...
06/05/2018 ∙ by Jian Ren, et al. ∙ 0 ∙ share

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E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs

Recurrent Neural Networks (RNNs) are becoming increasingly important for...
12/12/2018 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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CircConv: A Structured Convolution with Low Complexity

Deep neural networks (DNNs), especially deep convolutional neural networ...
02/28/2019 ∙ by Siyu Liao, et al. ∙ 0 ∙ share

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Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation

Accurate multi-organ abdominal CT segmentation is essential to many clin...
04/12/2019 ∙ by Yuyin Zhou, et al. ∙ 0 ∙ share

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C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs

Recently, significant accuracy improvement has been achieved for acousti...
03/14/2018 ∙ by Shuo Wang, et al. ∙ 0 ∙ share

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Zhe Li

Featured Co-authors

Yi Yang

Li Fei-Fei

Alan Yuille

Meng Wang

Yanzhi Wang

Chao Wang

Jian Tang

Chang Liu

Yan Yan

Liang Zhao

Tianbao Yang

Learning From Brains How to Regularize Machines

Generative Adversarial Active Learning for Unsupervised Outlier Detection

Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network

Thoracic Disease Identification and Localization with Limited Supervision

A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer

CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices

SEP-Nets: Small and Effective Pattern Networks

Hardware-Driven Nonlinear Activation for Stochastic Computing Based Deep Convolutional Neural Networks

Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank

Unified Convergence Analysis of Stochastic Momentum Methods for Convex and Non-convex Optimization

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

Improved Dropout for Shallow and Deep Learning

Image Dataset for Visual Objects Classification in 3D Printing

Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework

Efficient Recurrent Neural Networks using Structured Matrices in FPGAs

C3PO: Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing

An Area and Energy Efficient Design of Domain-Wall Memory-Based Deep Convolutional Neural Networks using Stochastic Computing

A Framework in CRM Customer Lifecycle: Identify Downward Trend and Potential Issues Detection

Learning Topics using Semantic Locality

Towards Budget-Driven Hardware Optimization for Deep Convolutional Neural Networks using Stochastic Computing

An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints

A Unified Analysis of Stochastic Momentum Methods for Deep Learning

EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching

E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs

CircConv: A Structured Convolution with Low Complexity

Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation

C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs